B2B organizations and their teams remain siloed. All too often, we buy new technologies, services and data types (e.g., predictive, intent, firmographic, technographic, etc.) for individual use cases without ensuring our investments adhere to a larger, cohesive strategy benefitting the business overall and its customers. Such neglect undermines the potential value of our investments.
Over the past few months, I’ve attended meetings, spoke with sales and marketing practitioners, discussed trends with data vendors, and read industry articles to gather the key data practices used by successful B2B organizations. Five approaches to data stood out.
Approach Your Customer/Prospect Data With a Holistic Strategy
Leadership must be convinced that properly gathering, organizing and leveraging data according to a defined strategy is a priority. In some cases, typically with larger organizations, it’ll be increasingly important to create a centralized, cross-functional team focused solely on developing and executing this data strategy, ensuring the successful use of data across various teams.
Two examples of teams doing this well are SAP and Extreme Networks. SAP’s “Crystal Ball” program, led by Paul Logue and Franklin Herbas, has evolved into a sophisticated customer experience platform, weaving together numerous types of first- and third-party data and feeding the resulting insights to departments throughout the organization. Similarly, Extreme Networks has “Project Orion.” Led by Paul Green, Orion focuses on finding innovative ways to combine and leverage various data types for intelligent account targeting and prioritization.
Developing such a foundation will help ensure effective use of your data by mitigating inter-departmental confusion, redundancies, frustration and over-all inefficiency.
Related Article: The Key Elements of a Successful Intent Data Strategy
Focus on Processes First, Then the Data
Let’s assume your executive team has bought into the idea of a holistic data strategy and has even allocated resources to the effort. If you’re thinking it’s now time to go on a data-buying spree, pump the breaks. You need a plan — a map of processes the data will fuel.
As Michelle Scardino, senior director of Integrated Marketing at Veritas Technologies, recently stated during a panel discussion on the disconnect between adtech, martech and sales-tech, “A fool with a tool is still a fool.” Similarly, it’s unwise to onboard new data solutions without first understanding the processes they’re meant to improve.
A helpful practice: construct a high-level diagram for the customer lifecycle and all the processes various teams use to facilitate the customer journey. This is a daunting task. Yet, it’s why you should have a cross-functional team working on the project. You’ll want to identify priorities, gaps and shortcomings among these processes. These will serve to help prioritize data projects (i.e., use cases) and any future data investments.
Related Article: How the Customer Journey Will Evolve in the Next 5 Years
Organize Your Data and Align it to Processes
Auditing all the data you currently have (both first- and third-party data) is also a good idea. It’s helpful to use a framework here. Here’s a B2B data framework used by an increasing number of marketing and sales teams.
After you’ve organized your data, list each of the various use cases for which each set of data is currently leveraged. Then try to identify any gaps. Are there any use cases for which your current data could be used? Do any inefficiencies exist where investment in additional data types would be beneficial? For example, if your business development/sales development team is wasting time and effort trying to engage a large account list, it may make sense to invest in intent data to quickly prioritize accounts among that list.
Having your data organized and connected to specific processes and use cases will help you to better understand what’s working, why and ways to optimize efforts.
Related Article: Why Organizations Need to Clean Their Dirty Data
Remember That No One Type of Customer Data Is a Cure-All
A final point: be sure to avoid any vendor claiming their data is all you need.
As Sean Brierly, market manager, Demand Gen and Revenue Marketing at Cisco, recently said, “It's not just about one set of data …. It’s really about context and stitching data together to really understand our customers and our prospects.”
Most types of B2B marketing and sales data are complementary. The value of each is compounded when they’re smartly leveraged together. Franklin Herbas, SAP’s Crystal Ball program lead, explained this idea well:
“For me it was mind blowing that in this day and age, people don’t consider bringing together not only intent data but also technographic and firmographic data, and creating a score on top of it in order to provide the best buying signals possible. So, what we did was actually create that set of data, and got some data scientists behind this, and merged it with technographic data. So, we can tell you what the state of mind is of the customers at any point in time throughout the funnel.”
Further, it’s important to avoid the data siloes that often creep up between teams (or even within teams) over time. This was a challenge for Extreme Networks inside sale team. As Jake Radzevich, manager of America’s Inside Sales at Extreme, said recently:
“If you're a sales rep, and you go look at fit data, intent data and engagement data all on its own, that's a lot of information that you need to be working through as an individual person. So, I think the beauty of what we're doing with Orion is bringing predictive models, bringing the web research and engagement all into one actionable view within Salesforce.”
It’s kind of like managing a football team: orchestrating various types of data (positions) from varying vendors (specific players) for different use cases (plays) can have profoundly differing results. You should strive to optimize your combination for your specific business model, goals, strategies, tactics, etc.
Let the Data Drive New Ideas
It’s a bad idea to adopt new data without first having a solid strategy and processes in place. That being said, after you’ve executed your initial plans, don’t be afraid to let the data point you toward new use cases.
Similarly, don’t be afraid to make mistakes. They’re going to happen, and you’re going to learn from them. As Extreme Networks' Green said, “Fail fast, and move on.” In the long run, such failures will translate to value for your team and organization.
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